Something for the weekend

Read next

A good night’s sleep can be more important than you might realise when it comes to doing a good day’s work.

Research into sleep deprivation has revealed that insufficient rest can lead to deviant behaviour in the workplace, ranging from turning up late and going home early to rudeness, theft, vandalism and even violence.

They discovered that in both groups sleep deprivation led to an increase in unethical behaviour with the tired students behaving rudely to customers during the experiment and the nurses demonstrating a greater amount of deviant behaviour in their subsequent shift.

The academics stress that it is important for managers to recognise that tired employees could be more likely to behave unethically. Sleep deprivation is becoming increasingly common in sectors such as investment banking for example they say and with 24/7 access to emails sleep deprivation is more common that it was a decade ago.

Employers are recommended to ensure there are sufficient breaks and should also consider re-evaluating the workplace culture to see if the organisation is promoting long work hours.

The study - Examining the effects of sleep deprivation on workplace deviance: A self-regulatory perspective,” will be published in an upcoming Academy of Management Journal.

● Businesses such as gyms and mobile phone services rely heavily on customer loyalty. Membership fees and monthly contracts are the revenue backbone for these organisations and so any insight into customer loyalty is well received.

There are few guides to help businesses predict either how often a customer will use a service - such as the gym - or how much revenue renewing customers will generate.Now two marketing professors Eva Ascarza at Columbia Business School and Bruce Hardie at London Business School using customer commitment have created a forecasting model for businesses.

The academics have examined how commitment can drive both customer usage and renewal behaviour simultaneously by using a combination of modelling and estimation techniques. Their model not only predicts future revenue from active customers but also identifies those customers at risk of not renewing their membership by looking at changes in use as the contract expiry date approaches.

Using four years of transaction data the academics tested the model and additionally compared their predictions with records of actual member behaviour. They discovered they were able to forecast the future behaviour of customers with at least 97 per cent accuracy. Their model was also able to classify customers on the basis of their commitment to the service “proposing a new approach to segment the customer base”.